Methodological Innovation
The rapid evolution of food systems requires risk assessment approaches that are robust, adaptive, and forward-looking. IFoRAC promotes methodological innovation to strengthen the efficiency, consistency, and predictive value of food safety risk analysis, while remaining firmly grounded in internationally recognized frameworks.
Our approach focuses on enhancing scientific workflows.
Advanced Analytical Approaches
IFoRAC supports the use of advanced analytical tools, including artificial intelligence and machine learning, to assist risk assessors in:
- Systematic literature screening
- Data integration and pattern recognition
- Exploration of large exposure and occurrence datasets
These approaches can significantly reduce the time required for evidence gathering, allowing experts to focus on scientific interpretation and decision-relevant conclusions.
New Approach Methodologies (NAMs)
IFoRAC follows developments in New Approach Methodologies, including:
- In vitro assays
- In silico modelling
- Read-across and mechanistic approaches
When applied appropriately, these methods provide mechanistic insights into hazards, complementing traditional evidence streams and supporting the gradual reduction of reliance on animal testing, in line with international scientific and ethical objectives.
Data Integration and Open Platforms
IFoRAC promotes the development of shared, interoperable data environments that integrate:
- Occurrence data
- Food consumption data
- Toxicological and hazard information
Such platforms enable cross-country analyses, improved comparability, and more coherent interpretation of evidence across regions.
Why this matters
Methodological innovation at IFoRAC is designed to augment scientific expertise, not automate decisions. By improving data handling, integration, and analytical efficiency, these approaches help ensure that risk assessments remain transparent, interpretable, and fit for regulatory decision-making in an increasingly complex food safety landscape.